Contour Detection by CORF Operator

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Abstract

We propose a contour operator, called CORF, inspired by the properties of simple cells in visual cortex. It combines, by a weighted geometric mean, the blurred responses of difference-of-Gaussian operators that model cells in the lateral geniculate nucleus (LGN). An operator that has gained particular popularity as a computational model of a simple cell is based on a family of Gabor Functions (GFs). However, the GF operator short-cuts the LGN, and its effectiveness in contour detection tasks, which is assumed to be the primary biological role of simple cells, has never been compared with the effectiveness of alternative operators. We compare the performances of the CORF and the GF operators using the RuG and the Berkeley data sets of natural scenes with associated ground truths. The proposed CORF operator outperforms the GF operator (RuG: t(39) = 4.39, p < 10(-4) and Berkeley: t(499) = 4.95, p < 10(-6)).
Original languageEnglish
Title of host publicationLecture Notes in Computer Science
PublisherSpringer
Pages395-402
Number of pages8
Publication statusPublished - 2012

Keywords

  • Contour detection
  • Gabor function
  • LGN
  • Simple cell
  • brain-inspired
  • orientation-selective

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